Next Article in Journal
Phytoplankton Biomass Dynamics in the Strait of Malacca within the Period of the SeaWiFS Full Mission: Seasonal Cycles, Interannual Variations and Decadal-Scale Trends
Previous Article in Journal
Improving Remote Species Identification through Efficient Training Data Collection
Remote Sens. 2014, 6(4), 2699-2717; doi:10.3390/rs6042699
Article

Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods

1
,
2
,
1,* , 2
 and
3
1 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China 2 School of Resource and Environmental Sciences & Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, Wuhan 430079, China 3 School of Architecture & Urban Planning, Shenzhen University, Shenzhen 518060, China
* Author to whom correspondence should be addressed.
Received: 13 January 2014 / Revised: 10 March 2014 / Accepted: 17 March 2014 / Published: 25 March 2014
View Full-Text   |   Download PDF [780 KB, 19 June 2014; original version 19 June 2014]   |   Browse Figures
SciFeed

Abstract

With 298 heterogeneous soil samples from Yixing (Jiangsu Province), Zhongxiang and Honghu (Hubei Province), this study aimed to combine a successive projections algorithm (SPA) with a support vector machine regression (SVMR) model (SPA-SVMR model) to improve the estimation accuracy of soil organic carbon (SOC) contents using the laboratory-based visible and near-infrared (VIS/NIR, 350−2500 nm) spectroscopy of soils. The effects of eight spectra pre-processing methods, i.e., Log (1/R), Log (1/R) coupled with Savitzky-Golay (SG) smoothing (Log (1/R) + SG), first derivative with SG smoothing (FD), second derivative with SG smoothing (SD), SG, standard normal variate (SNV), mean center (MC) and multiplicative scatter correction (MSC), on SPA-based informative wavelength selection were explored. The SVMR model (i.e., SVMR without SPA) and SPA-PLSR model (i.e., SPA combined with partial least squares regression (PLSR)) were developed and compared with the SPA-SVMR model in order to evaluate the performance of SPA-SVMR. The results indicated that the variables selected by SPA and their distributions were strongly affected by different pre-processing methods, and SG was the optimal pre-processing method for SPA-SVMR model development; the SPA-SVMR model using SG pre-processing and 28 SPA-selected wavelengths obtained a better result (R2V = 0.73, RMSEV = 2.78 g∙kg−1 and RPDV = 1.89) and outperformed the SVMR model (R2V = 0.72, RMSEV = 2.83 g∙kg−1 and RPDV = 1.86) and the SPA-PLSR model (R2V = 0.62, RMSEV = 3.23 g∙kg−1 and RPDV = 1.63). Most of the spectral bands used by the SPA-SVMR model over the near-infrared region were important wavelengths for SOC content estimation. This study demonstrated that the combination of SPA and SVMR is feasible and reliable for estimating SOC content from the VIS/NIR spectra of soils in regions with multiple soil and land-use types.
Keywords: soil quality; remote sensing; spectra pre-processing; variable selection soil quality; remote sensing; spectra pre-processing; variable selection
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote |
RIS
MDPI and ACS Style

Peng, X.; Shi, T.; Song, A.; Chen, Y.; Gao, W. Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods. Remote Sens. 2014, 6, 2699-2717.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert